Two major strategies are used when training a MTL algorithm, hard parameters sharing [1]} or soft parameters sharing [2]}. Hard parameter sharing is the most commonly used approach to MTL in neural networks and greatly reduces the risk of overfitting [2]}, see figure REF . In this work we utilize hard parameters sharing due to its great performance and wide utilization.
